Metaflow

What is Metaflow?

Metaflow is an open-source Python library for developing, deploying and operating data-intensive projects such as machine learning, AI and data science. Netflix built it and open-sourced it in 2019, and it is released under the Apache License 2.0. You write a workflow as plain Python, run and debug it on a laptop, then scale the same code out to GPUs, many cores or several instances, and deploy it to a production orchestrator without rewriting it. Metaflow versions the variables inside each flow automatically for experiment tracking, helps manage Python dependencies in local and cloud runs, and lets flows react to events. It works with your own infrastructure: AWS (EKS, S3, Batch, Step Functions), Azure (AKS, Blob Storage), Google Cloud (GKE, Cloud Storage) or any Kubernetes cluster, including on-premise ones. Metaflow itself is free to install with pip, and a browser-based Metaflow Sandbox is offered for trying the cloud stack at no cost.

Use cases

• Build ML pipelines in plain Python
• Scale training jobs to cloud GPUs
• Track experiments and flow versions
• Deploy workflows to production orchestrators
• Trigger flows from data or other events
• Prototype locally and promote to the cloud
• Orchestrate GenAI and data science projects

Who it's for

• ML engineers
• Data scientists
• AI engineers
• Platform engineers

Why use Metaflow

• The library is free under Apache 2.0, so there is no per-seat or per-run license cost.
• The same Python code moves from laptop to cloud to production without changes.
• Automatic versioning of flow variables makes experiments easier to debug and compare.
• It runs in your own AWS, Azure, Google Cloud or Kubernetes account, which keeps data under your governance rules.
• One API covers modeling, orchestration, compute and data, which cuts the number of separate tools to manage.

Metaflow features

• Workflows written in plain Python using any libraries
• Local development with the same code used in production
• Automatic versioning of variables and artifacts in each flow
• Dependency management for local and cloud runs, including uv support
• Cloud compute on AWS Batch or Kubernetes with GPUs and many cores
• Deployment to Argo Workflows, AWS Step Functions or Apache Airflow
• Event-based triggers to connect flows
• Conditional and recursive steps in flows
• Incremental flow authoring with the spin command
• Data access patterns for warehouses and lakes

Metaflow integrations

• AWS Batch
• AWS Step Functions
• Argo Workflows
• Apache Airflow
• Kubernetes
• Azure Blob Storage
• Google Cloud Storage
• Kubeflow

How to get started with Metaflow

1. Install Metaflow locally with pip install metaflow.
2. Write a flow as a Python class with steps, using any libraries you like.
3. Run and debug the flow on your machine, and inspect stored results or use notebooks.
4. Deploy the Metaflow stack on AWS, Azure, Google Cloud or Kubernetes to run steps on remote compute.
5. Schedule the flow on a production orchestrator and connect it to other flows through events.

Metaflow limitations

• Production use needs infrastructure that you set up in your own cloud account
• Local-only installs do not provide external compute scaling or production-grade deployment
• A central metadata service is needed for multi-person collaboration

Metaflow pricing

Metaflow is open source, so you can use and self-host it for free.

Prices can change and may vary by country or exclude tax. Check the official pricing page.

Metaflow alternatives

The main Metaflow alternatives listed on Debuly are ZenML, Kubeflow, MLflow, Windmill, AG2 and Agent Development Kit (ADK). The table compares pricing and platforms; order is A–Z.

1. 

   AG2

   Open-source multi-agent framework, formerly AutoGen, plus an AgentOS platform for coordinating and deploying teams of AI agents.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit AG2 (official site)
2. 

   Agent Development Kit (ADK)

   Google's open-source, code-first Python framework for building, testing and deploying AI agents and multi-agent workflows.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit Agent Development Kit (ADK) (official site)
3. 

   Kubeflow

   Open source, Kubernetes-native AI platform made of modular projects for training, tuning, pipelines, notebooks and model management.

   Developer platformsAutomation and agents
   Open sourceVisit Kubeflow (official site)
4. 

   MLflow

   Open-source AI engineering platform for tracing, evaluating and deploying LLM agents, apps and classic ML models.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit MLflow (official site)
5. 

   Windmill

   Open-source, self-hostable platform for building internal tools, workflows and data pipelines as code.

   Developer platformsAutomation and agentsCoding
   FreemiumVisit Windmill (official site)
6. 

   ZenML

   Open-source orchestration for Python ML pipelines and AI agents, with Kitaru for recording and replaying agent runs against production data.

   Developer platformsAutomation and agents
   Open sourceVisit ZenML (official site)

Metaflow FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from metaflow.org · fetched 24 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

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